class_docstr: An implementation of `"NEU" <https://www.ijcai.org/Proceedings/2017/0544.pdf>`_
  from the IJCAI 17 paper "Fast Network Embedding Enhancement via High Order Proximity Approximation".
  The procedure uses an arbitrary embedding and augments it by higher order proximities with a recursive
  meta learning algorithm.
  
  Args:
      L1 (float): Weight of lower order proximities. Defauls is 0.5
      L2 (float): Weight of higher order proximities. Default is 0.25.
      T (int): Number of iterations. Default is 1.
      seed (int): Random seed value. Default is 42.
code: def __init__(
      self,
      L1: float = 0.5,
      L2: float = 0.25,
      T: int = 1,
      seed: int = 42,
  ):
      self.iterations = T
      self.L1 = L1
      self.L2 = L2
      self.seed = seed
  
file_path: karateclub/karateclub/node_embedding/meta/neu.py
imports: import numpy
  import networkx
  from sklearn.preprocessing import normalize
  from karateclub.estimator import Estimator
label_desc: Use image node_id 1 to create a new NEU object from inherited base classes: Estimator with example: obj = NEU(L1, L2, T, seed)